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How the Anti-TrustRank Algorithm Can Help to Protect the Reputation of Financial Institutions

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Research Challenges in Information Science (RCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

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Abstract

When financial institutions are found to have their customers conduct money laundering through them, they are subjected to large fines. Moreover, the reputation of those institutions suffers greatly through public exposure. Consequently, financial institutions invest significant resources in building systems to automatically detect money laundering in order to minimize the negative impact of money launderers on their reputation. This paper investigates a graph algorithm called Anti-TrustRank and demonstrates how it can be used to identify money launderers. Our approach to using Anti-TrustRank is not replacing money laundering detection systems, rather is generating additional inputs to feed into such systems in order to improve their overall detection accuracy.

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Acknowledgement

Irina Astrova’s work was supported by the Estonian Ministry of Education and Research institutional research grant IUT33-13.

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Authors and Affiliations

  1. Department of Software Science, School of IT, Tallinn University of Technology, Akadeemia tee 21, 12618, Tallinn, Estonia

    Irina Astrova

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  1. Irina Astrova
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Correspondence to Irina Astrova .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Astrova, I. (2020). How the Anti-TrustRank Algorithm Can Help to Protect the Reputation of Financial Institutions. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_30

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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